Machine Learning

 Data normalization or standardization is defined as the process of rescaling original data without changing its behavior or nature. We define new boundary (most common is (0,1),(-1,1)) and convert data accordingly." " "

Decimal normalization is a method of normalization in which the given value is normalized by shifting the decimal points of that value. The number of decimal points to move is determined by the absolute maximum value of the given set of data."

This article explains min-max normalization. Min-Max normalization performs on original data a linear transformation. You will find the min-max normalization formula with proper explanations and examples. Python code for min-max normalization is also included."

n Z score normalization, the values are normalized based on the mean and standard deviation of attribute A. For Vi  value of attribute A, normalized value Ui is given as,"

Cross-validation is a technique to evaluate the predictive models by splitting the original training data sample into a training set to train the model, and a test set to evaluate it. Cross-validation is a re-sampling process used to evaluate the model if we have limited amount of data"